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@article{204463,
        author = {Abhiram S and Amaan Liyah and Pranave Kannan and Sooraj S and Yatheesh S and Nikhila A},
        title = {Realtime Rendering with Ml-Enhanced Materials},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {141-145},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204463},
        abstract = {Real-time rendering is of paramount importance for modern applications such as gaming, virtual reality and interactive simulations. Achieving high visual realism under tight performance constraints remains a significant challenge. Common rendering pipelines based on shaders rely on manually defined material models that often lack the flexibility and accuracy required to efficiently capture complex light-material interactions. In this paper we present a novel approach of applying machine learning techniques in real time shader systems to improve material representation and rendering quality. This approach uses a trained neural network to simulate complex material properties such as reflection, displacement, and texture details, giving a realistic output while keeping real-time performance. This system consists of machine learning models integrated into the shader pipeline, which give material responses to changes in environment lighting and other scene conditions. This research shows that an ML-enhanced material system provides better output than traditional shader techniques with small computational overhead and minimal impact on frame rates.},
        keywords = {Computer Graphics, Deep Learning/ Machine Learning, Neural Rendering, Physically Based Rendering (PBR), Shaders},
        month = {June},
        }